app.py

import os
import re
import gradio as gr
import requests
import pandas as pd
import heapq
from collections import Counter
from io import BytesIO
from youtube_transcript_api import YouTubeTranscriptApi
from smolagents import tool, Tool, CodeAgent, DuckDuckGoSearchTool, HfApiModel, VisitWebpageTool, SpeechToTextTool, FinalAnswerTool
from langchain_community.document_loaders import WikipediaLoader, PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Use the new import for HuggingFaceEmbeddings
from langchain_huggingface import HuggingFaceEmbeddings # <--- IMPORTANT: Updated import
from langchain_community.vectorstores import DocArrayInMemorySearch
from langchain_core.documents import Document
from dotenv import load_dotenv
import tempfile
import mimetypes
import logging
import uuid
# For timeout functionality
import concurrent.futures
import time

# Import DocList from docarray
from docarray import DocList # <--- IMPORTANT: Added this import


# --- Initialize logging ---
LOG_FILE_PATH = "agent_activity.log"
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    filename=LOG_FILE_PATH,
    filemode='a'
)
logger = logging.getLogger(__name__)

# --- Load environment variables ---
load_dotenv()
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
HF_EMBEDDING_MODEL_ID = os.getenv("HF_EMBEDDING_MODEL_ID", "sentence-transformers/all-MiniLM-L6-v2")

if not HF_API_TOKEN:
    logger.error("HF_API_TOKEN not found in environment variables! Please set it to use the HfApiModel.")

# --- Global Vector Store and Embeddings ---
try:
    embeddings = HuggingFaceEmbeddings(model_name=HF_EMBEDDING_MODEL_ID)
    logger.info(f"Initialized HuggingFaceEmbeddings with model: {HF_EMBEDDING_MODEL_ID}")
except Exception as e:
    logger.error(f"Failed to initialize HuggingFaceEmbeddings: {e}. Please ensure the model_id is correct and dependencies are installed.")
    embeddings = None

# Initialize DocArrayInMemorySearch WITH the required arguments: doc_index and embedding
vectorstore = DocArrayInMemorySearch(doc_index=DocList(), embedding=embeddings) if embeddings else None # <--- FIXED THIS LINE
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    length_function=len,
    is_separator_regex=False,
)
logger.info("Initialized in-memory DocArrayInMemorySearch vector store and RecursiveCharacterTextSplitter.")


# --- Utility Functions ---
def extract_youtube_id(url: str) -> str:
    """Extract YouTube ID from various URL formats"""
    patterns = [
        r'(?:https?:\/\/)?(?:www\.)?youtube\.com\/watch\?v=([^&]+)',
        r'(?:https?:\/\/)?youtu\.be\/([^?]+)',
        r'([a-zA-Z0-9_-]{11})'
    ]
    for pattern in patterns:
        match = re.search(pattern, url)
        if match:
            return match.group(1)
    return ""

def add_document_to_vector_store(content: str, source: str, metadata: dict = None):
    """
    Adds content to the global vector store.
    Chunks the content and creates LangChain Documents.
    """
    if vectorstore is None:
        logger.warning("Vector store not initialized. Cannot add document.")
        return

    try:
        chunks = text_splitter.split_text(content)
        docs = []
        for i, chunk in enumerate(chunks):
            doc_metadata = {"source": source, "chunk_index": i}
            if metadata:
                doc_metadata.update(metadata)
            docs.append(Document(page_content=chunk, metadata=doc_metadata))
        
        # When vectorstore is initialized with embedding, add_documents might not need it again.
        # But explicitly passing it is safer if there are multiple ways to initialize.
        vectorstore.add_documents(docs) # No `embedding` argument needed here if initialized in __init__
        logger.info(f"Added {len(docs)} chunks from '{source}' to the vector store.")
    except Exception as e:
        logger.error(f"Error adding document from '{source}' to vector store: {e}")

# --- Enhanced Tools ---
class WikiSearchTool(Tool):
    """Enhanced Wikipedia search with better formatting and error handling"""
    name = "wiki_search"
    description = "Search Wikipedia for a query. Returns up to 2 results with metadata."
    inputs = {"query": {"type": "string", "description": "Search term for Wikipedia"}}
    output_type = "string"

    def forward(self, query: str) -> str:
        try:
            logger.info(f"Searching Wikipedia for: {query}")
            docs = WikipediaLoader(query=query, load_max_docs=2).load()
            if not docs:
                logger.info(f"No Wikipedia articles found for: {query}")
                return "No Wikipedia articles found."
                
            formatted_results = []
            for i, doc in enumerate(docs):
                summary = doc.page_content[:1000] + "..." if len(doc.page_content) > 1000 else doc.page_content
                
                add_document_to_vector_store(
                    content=doc.page_content,
                    source=doc.metadata.get('source', 'Wikipedia'),
                    metadata={"title": doc.metadata.get('title', 'N/A')}
                )

                formatted_results.append(
                    f"--- Wikipedia Result {i+1} ---\n"
                    f"Title: {doc.metadata.get('title', 'N/A')}\n"
                    f"URL: {doc.metadata.get('source', 'N/A')}\n"
                    f"Summary: {summary}\n"
                )
            return "\n\n".join(formatted_results)
        except Exception as e:
            logger.error(f"Wikipedia search error for '{query}': {e}")
            return f"Wikipedia search error: {str(e)}"

class FileAnalysisTool(Tool):
    """Universal file analyzer for text/PDF/Excel files. Content added to vector store."""
    name = "file_analysis"
    description = "Analyze text, PDF, and Excel files. Returns extracted content. Text and PDF content is also indexed for future retrieval."
    inputs = {"file_path": {"type": "string", "description": "Path to the local file"}}
    output_type = "string"

    def forward(self, file_path: str) -> str:
        if not os.path.exists(file_path):
            return f"File not found: {file_path}"

        try:
            mime_type, _ = mimetypes.guess_type(file_path)
            logger.info(f"Analyzing file: {file_path} with MIME type: {mime_type}")
            
            content = ""
            if mime_type == "application/pdf":
                content = self._process_pdf(file_path)
            elif mime_type in ["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "application/vnd.ms-excel"]:
                content = self._process_excel(file_path)
            elif mime_type and ("text" in mime_type or "csv" in mime_type):
                content = self._process_text(file_path)
            else:
                return f"Unsupported file type for analysis: {mime_type}. Only PDF, Excel, and text/CSV files are supported."

            if mime_type in ["application/pdf", "text/plain", "text/csv"]:
                add_document_to_vector_store(
                    content=content,
                    source=f"file:{os.path.basename(file_path)}",
                    metadata={"file_path": file_path, "mime_type": mime_type}
                )
            
            return content

        except Exception as e:
            logger.error(f"File analysis error for '{file_path}': {e}")
            return f"File analysis error: {str(e)}"

    def _process_pdf(self, path: str) -> str:
        loader = PyPDFLoader(path)
        docs = loader.load()
        content = "\n\n".join([doc.page_content for doc in docs])
        if len(content) > 8000:
            logger.warning(f"PDF content truncated from {len(content)} to 8000 characters for {path}")
            return content[:8000] + "\n... [Content truncated]"
        return content

    def _process_excel(self, path: str) -> str:
        df = pd.read_excel(path)
        info = BytesIO()
        df.info(buf=info)
        info_str = info.getvalue().decode('utf-8')
        
        return (f"Excel file loaded. First 10 rows:\n{df.head(10).to_markdown()}\n\n"
                f"DataFrame Info:\n{info_str}")

    def _process_text(self, path: str) -> str:
        with open(path, 'r', encoding='utf-8') as f:
            content = f.read()
            if len(content) > 8000:
                logger.warning(f"Text file content truncated from {len(content)} to 8000 characters for {path}")
                return content[:8000] + "\n... [Content truncated]"
            return content

class VideoTranscriptionTool(Tool):
    """Enhanced YouTube transcription with multilingual support and better output. Transcribed content is added to vector store."""
    name = "transcript_video"
    description = "Fetch YouTube video transcripts with optional timestamps. Supports English, French, Spanish, German. Transcribed text is indexed for future retrieval."
    inputs = {
        "url": {"type": "string", "description": "YouTube URL or ID"},
        "include_timestamps": {"type": "boolean", "description": "Include timestamps? (default: False)"}
    }
    output_type = "string"

    def forward(self, url: str, include_timestamps: bool = False) -> str:
        try:
            video_id = extract_youtube_id(url)
            if not video_id:
                return "Invalid YouTube URL or ID format. Please provide a valid YouTube URL or an 11-character video ID."
                
            logger.info(f"Attempting to transcribe video ID: {video_id}")
            transcript_list = YouTubeTranscriptApi.get_transcript(
                video_id, 
                languages=['en', 'fr', 'es', 'de']
            )
            
            if not transcript_list:
                return f"No transcript found for video ID: {video_id} in supported languages (en, fr, es, de)."

            full_transcript_text = " ".join(seg['text'] for seg in transcript_list)
            
            add_document_to_vector_store(
                content=full_transcript_text,
                source=f"youtube_video:{video_id}",
                metadata={"video_url": url}
            )

            if include_timestamps:
                formatted_transcript = "\n".join(
                    f"[{int(seg['start']//60):02d}:{int(seg['start']%60):02d}] {seg['text']}"
                    for seg in transcript_list
                )
            else:
                formatted_transcript = full_transcript_text
            
            return formatted_transcript
        except Exception as e:
            logger.error(f"Transcription error for '{url}': {e}")
            return f"Transcription error: {str(e)}. This might be due to no available transcript or an unsupported video."

class DataAnalysisTool(Tool):
    """Perform data analysis using pandas on structured data (CSV/Excel)"""
    name = "data_analysis"
    description = "Analyze CSV/Excel data using pandas operations. Supported operations: 'describe', 'groupby:column:aggfunc' (e.g., 'groupby:Category:mean'). Outputs are NOT added to vector store."
    inputs = {
        "file_path": {"type": "string", "description": "Path to the local data file (CSV or Excel)"},
        "operation": {"type": "string", "description": "Pandas operation (e.g., 'describe', 'groupby:column_name:agg_function')"}
    }
    output_type = "string"

    def forward(self, file_path: str, operation: str) -> str:
        if not os.path.exists(file_path):
            return f"File not found: {file_path}"

        try:
            if file_path.endswith('.csv'):
                df = pd.read_csv(file_path)
            elif file_path.endswith('.xlsx') or file_path.endswith('.xls'):
                df = pd.read_excel(file_path)
            else:
                return "Unsupported file format for data analysis. Please provide a .csv or .xlsx file."
            
            logger.info(f"Performing data analysis operation '{operation}' on {file_path}")

            if operation == "describe":
                return "Descriptive Statistics:\n" + str(df.describe())
            elif operation.startswith("groupby:"):
                parts = operation.split(":")
                if len(parts) == 3:
                    _, col, agg = parts
                    if col not in df.columns:
                        return f"Column '{col}' not found in the DataFrame."
                    try:
                        result = df.groupby(col).agg(agg)
                        return f"Groupby operation '{agg}' on column '{col}':\n" + str(result)
                    except Exception as agg_e:
                        return f"Error performing aggregation '{agg}' on column '{col}': {str(agg_e)}"
                else:
                    return "Invalid 'groupby' operation format. Use 'groupby:column_name:agg_function'."
            else:
                return "Unsupported operation. Try: 'describe' or 'groupby:column_name:agg_function'."
        except Exception as e:
            logger.error(f"Data analysis error for '{file_path}' with operation '{operation}': {e}")
            return f"Data analysis error: {str(e)}. Please check file content and operation."

class RetrievalTool(Tool):
    """
    Retrieves relevant information from the in-memory vector store based on a query.
    This tool allows the agent to access previously processed documents and transcripts.
    """
    name = "retrieve_from_vector_store"
    description = "Search for relevant information within previously processed documents and transcripts using a semantic query. Returns top K relevant chunks."
    inputs = {
        "query": {"type": "string", "description": "The semantic query to search the vector store."},
        "k": {"type": "integer", "description": "Number of top results to retrieve (default: 3)", "default": 3}
    }
    output_type = "string"

    def forward(self, query: str, k: int = 3) -> str:
        if vectorstore is None or embeddings is None:
            return "Vector store is not initialized or embeddings are missing. No documents available for retrieval."
        
        try:
            logger.info(f"Retrieving {k} chunks from DocArrayInMemorySearch for query: {query}")
            # Ensure similarity_search uses the vectorstore's internal embedding if initialized correctly
            # or if it takes an explicit embedding argument here.
            # With DocArrayInMemorySearch initialized with `embedding=embeddings`, this call should be fine.
            retrieved_docs = vectorstore.similarity_search(query, k=k) 
            
            if not retrieved_docs:
                return "No relevant information found in the vector store for this query."
            
            formatted_results = []
            for i, doc in enumerate(retrieved_docs):
                source = doc.metadata.get('source', 'Unknown Source')
                title = doc.metadata.get('title', 'N/A')
                chunk_index = doc.metadata.get('chunk_index', 'N/A')
                formatted_results.append(
                    f"--- Retrieved Document Chunk {i+1} ---\n"
                    f"Source: {source} (Chunk: {chunk_index})\n"
                    f"Title: {title}\n"
                    f"Content: {doc.page_content}\n"
                )
            return "\n\n".join(formatted_results)
        except Exception as e:
            logger.error(f"Error retrieving from vector store for query '{query}': {e}")
            return f"Error retrieving from vector store: {str(e)}"

class ChessAnalysisAPITool(Tool):
    """
    Analyzes a chess position provided in FEN format using a remote chess engine API (chess-api.com).
    """
    name = "analyze_chess_position_api"
    description = (
        "Analyze a chess position provided in FEN (Forsyth-Edwards Notation) format using an online engine. "
        "Returns the best move in algebraic notation for the current player, along with evaluation."
        "Note: This tool cannot interpret chess positions directly from images. "
        "The FEN string must be provided by the user."
    )
    inputs = {
        "fen_string": {"type": "string", "description": "The chess position in FEN format. Example: 'rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1'"},
        "depth": {"type": "integer", "description": "The analysis depth for the chess engine (higher means better, but slower; max ~18 for this API; default: 15)", "default": 15}
    }
    output_type = "string"

    def forward(self, fen_string: str, depth: int = 15) -> str:
        actual_depth = min(depth, 18) 
        
        try:
            logger.info(f"Analyzing FEN: {fen_string} at depth {actual_depth} using chess-api.com.")
            
            response = requests.post(
                "https://chess-api.com/v1",
                json={"fen": fen_string, "depth": actual_depth}
            )
            response.raise_for_status()
            data = response.json()

            if data.get("type") == "bestmove":
                move_san = data.get("san", data.get("move"))
                evaluation = data.get("eval")
                mate_in_moves = data.get("mate")

                result = f"Best move: **{move_san}** (UCI: {data.get('move')})\n"
                
                if mate_in_moves is not None:
                    player_to_move = "White" if data.get("turn") == 'w' else "Black"
                    result += f"Forced mate for {player_to_move} in {abs(mate_in_moves)} moves.\n"
                elif evaluation is not None:
                    eval_str = ""
                    if evaluation >= 1000:
                        eval_str = "Decisive advantage for White"
                    elif evaluation <= -1000:
                        eval_str = "Decisive advantage for Black"
                    elif evaluation > 0:
                        eval_str = f"White is up by {evaluation} centipawns"
                    elif evaluation < 0:
                        eval_str = f"Black is up by {abs(evaluation)} centipawns"
                    else:
                        eval_str = "Even position"
                    result += f"Evaluation: {eval_str} (Depth: {data.get('depth')})\n"
                
                result += "(Source: chess-api.com - Stockfish 17 NNUE)"
                return result
            else:
                return f"Chess API response: {data.get('text', 'No best move found or error.')}"

        except requests.exceptions.RequestException as e:
            logger.error(f"Error communicating with remote chess analysis API for FEN '{fen_string}': {e}")
            return f"Error contacting remote chess analysis API: {str(e)}. Please try again later."
        except Exception as e:
            logger.error(f"An unexpected error occurred during remote chess analysis for FEN '{fen_string}': {e}")
            return f"An unexpected error occurred during chess analysis: {str(e)}"


# --- Agent Initialization ---
class BasicAgent:    
    def __init__(self):
        self.model = HfApiModel(
            temperature=0.0,
            token=os.environ.get("HF_API_TOKEN"),
            max_tokens=2000
        )
        
        self.tools = self._initialize_tools()
        self.agent = self._create_agent()

        
    def _initialize_tools(self) -> list:
        """Initialize all tools with enhanced capabilities"""
        base_tools = [
            DuckDuckGoSearchTool(),
            WikiSearchTool(),
            VisitWebpageTool(),
            SpeechToTextTool(), 
            FinalAnswerTool(),
            VideoTranscriptionTool(),
            FileAnalysisTool(),
            DataAnalysisTool(),
            self._create_excel_download_tool(),
            self._create_keywords_tool(),
            ChessAnalysisAPITool(),
        ]
        
        if vectorstore and embeddings:
            logger.info("Adding RetrievalTool to the agent's tools.")
            base_tools.append(RetrievalTool())
        else:
            logger.warning("RetrievalTool not added because vector store or embeddings are not initialized.")              

        return base_tools
    
    def _create_excel_download_tool(self):
        """Tool to download and parse Excel files from a specific URL"""
        @tool
        def download_and_parse_excel(task_id: str) -> dict:
            """
            Downloads an Excel file from a predefined URL using a task_id and parses its content.
            Returns a dictionary with status and data (first 10 rows), columns, and shape.
            """
            try:
                url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
                logger.info(f"Attempting to download Excel from: {url}")
                response = requests.get(url, timeout=60)
                response.raise_for_status()
                
                with tempfile.NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
                    tmp.write(response.content)
                    temp_file_path = tmp.name
                
                df = pd.read_excel(temp_file_path)
                os.unlink(temp_file_path)

                logger.info(f"Successfully downloaded and parsed Excel for task_id: {task_id}")
                return {
                    "task_id": task_id,
                    "data_sample": df.head(10).to_dict(orient="records"),
                    "status": "Success",
                    "columns": df.columns.tolist(),
                    "shape": df.shape
                }
            except requests.exceptions.RequestException as req_err:
                logger.error(f"Network or HTTP error downloading Excel for task_id '{task_id}': {req_err}")
                return {"status": f"Download error: {str(req_err)}"}
            except Exception as e:
                logger.error(f"Error parsing Excel for task_id '{task_id}': {e}")
                return {"status": f"Parsing error: {str(e)}"}
        return download_and_parse_excel
    
    def _create_keywords_tool(self):
        """Keywords extractor with TF-IDF like scoring (basic frequency for now)"""
        @tool
        def extract_keywords(text: str, top_n: int = 5) -> list:
            """
            Extracts the most frequent keywords from a given text, excluding common stopwords.
            Args:
                text (str): The input text to extract keywords from.
                top_n (int): The number of top keywords to return.
            Returns:
                list: A list of the most frequent keywords.
            """
            if not text:
                return []
            
            stopwords = set([
                "a", "an", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it",
                "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these",
                "they", "this", "to", "was", "will", "with", "he", "she", "it's", "i", "we", "you", "my",
                "your", "our", "us", "him", "her", "his", "hers", "its", "them", "their", "what", "when",
                "where", "why", "how", "which", "who", "whom", "can", "could", "would", "should", "may",
                "might", "must", "have", "has", "had", "do", "does", "did", "am", "are", "is", "were", "been",
                "being", "from", "up", "down", "out", "off", "over", "under", "again", "further", "then",
                "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", "few",
                "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so",
                "than", "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"
            ])
            
            words = re.findall(r'\b\w+\b', text.lower())
            filtered = [w for w in words if w not in stopwords and len(w) > 2]
            counter = Counter(filtered)
            return [word for word, _ in counter.most_common(top_n)]
        return extract_keywords

    def _create_agent(self) -> CodeAgent:
        """Create agent with improved system prompt"""
        system_prompt = """
You are an advanced, helpful, and highly analytical research assistant. Your goal is to provide accurate, comprehensive, and well-structured answers to user queries, leveraging all available tools efficiently.

**Follow this robust process:**

1.  **Understand the User's Need:** Carefully analyze the user's question, including any attached files or specific requests (e.g., "summarize," "analyze data," "find facts").
2.  **Formulate a Detailed Plan:** Before acting, create a clear, step-by-step plan. This plan should outline:
    * What information needs to be gathered.
    * Which tools are most appropriate for each step.
        * Use `retrieve_from_vector_store` first if the query seems to be related to previously processed information (e.g., "What did we learn about X from the uploaded document?").
        * Use `duckduckgo_search` for general web search.
        * Use `wiki_search` for encyclopedic facts.
        * Use `transcript_video` for YouTube video content.
        * Use `file_analysis` to inspect content of local files.
        * Use `data_analysis` for structured analysis of CSV/Excel files.
        * Use `analyze_chess_position_api` if the user provides a FEN string for a chess position and asks for the best move.
    * How you will combine information from different sources.
    * How you will verify or synthesize the findings.
3.  **Execute the Plan Using Tools:** Call the necessary tools, providing clear and correct arguments. If a tool fails, try to understand why and adapt your plan (e.g., try a different search query or tool).
4.  **Synthesize and Verify Information:** Once you have gathered sufficient information, synthesize it into a coherent answer. Do not just list facts; explain their significance and how they relate to the original question. If there are contradictions or uncertainties, mention them.
5.  **Formulate the Final Answer:**
    * Present your answer clearly and concisely.
    * Always begin your ultimate response with "FINAL ANSWER:".
    * If the answer is a single number, provide only the number.
    * If the answer is a list, provide comma-separated values.
    * For complex answers, use structured formats like bullet points or JSON where appropriate to enhance readability.
    * **Crucially, always include sources or references (e.g., URLs, Wikipedia titles, file names, "Internal Knowledge Base", "Remote Chess API") where you obtained the information.** This builds trust and allows for verification.
    * If you used `file_analysis` or `data_analysis` tools on an uploaded file, explicitly state that you analyzed the provided file.

**Important Considerations:**
* **Prioritize:** If the query involves a specific file, start by analyzing that file if appropriate. If the query seems to refer to previously processed data, try `retrieve_from_vector_store` first.
* **Limitations:** If you cannot answer a question with the available tools, state that clearly.
* **Conciseness:** Be as concise as possible while providing an accurate answer.
"""
        agent = CodeAgent(
            model=self.model,
            tools=self.tools,
            add_base_tools=True,
            max_steps=15 # <--- Added this to limit agent's internal reasoning/tool-use steps
        )
        agent.prompt_templates["system_prompt"] = system_prompt
        return agent

        
    def __call__(self, question: str) -> str:
        logger.info(f"Received question: {question[:200]}...")
        print(f"Agent received question (first 50 chars): {question[:50]}...")

        try:
            global vectorstore
            # Re-initialize vectorstore for a new session without arguments
            # This relies on the add_documents and similarity_search methods getting the embedding
            if embeddings:
                vectorstore = DocArrayInMemorySearch() # <--- REVERTED TO THIS SIMPLE INIT HERE TOO
                logger.info("DocArrayInMemorySearch re-initialized for new session.")
            else:
                logger.warning("Embeddings not initialized, cannot re-initialize DocArrayInMemorySearch.")
                return "Error: Embedding model not loaded, cannot process request."



            # --- Implement a timeout for the agent's run method ---
            AGENT_TIMEOUT_SECONDS = 120  # Max time in seconds for the agent to respond
            
            with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
                future = executor.submit(self.agent.run, question)
                try:
                    response = future.result(timeout=AGENT_TIMEOUT_SECONDS)
                except concurrent.futures.TimeoutError:
                    logger.warning(f"Agent execution timed out after {AGENT_TIMEOUT_SECONDS} seconds for question: {question[:100]}...")
                    future.cancel() # Cancel the future if it's still running
                    return "Error: The agent took too long to respond and timed out. Please try again with a simpler query or check the input."
                except Exception as e:
                    # Catch any other exceptions that might occur during agent.run
                    logger.error(f"Agent execution failed during run for question '{question[:100]}': {str(e)}", exc_info=True)
                    return f"Error processing your request: {str(e)}. Please try again or rephrase your question."

            logger.info(f"Response generated successfully for question: {question[:200]}")
            # print statement for immediate console feedback of the final answer
            print(f"Agent returning answer: {response}")
            return response
        except Exception as e:
            # This outer catch is for issues before agent.run is called or unhandled by the ThreadPoolExecutor
            logger.error(f"Agent setup or execution failed (outer catch) for question '{question[:100]}': {str(e)}", exc_info=True)
            return f"Error processing your request: {str(e)}. Please try again or rephrase your question."
       

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username= f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
         print(f"Error decoding JSON response from questions endpoint: {e}")
         print(f"Response text: {response.text[:500]}")
         return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)